Can AI in Health Care Be Truly Inclusive?
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By
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Beth Rush
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June 22, 2026
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Clinical Scorecard: Is It Possible for Artificial Intelligence in Healthcare to Achieve Genuine Inclusivity?
At a Glance
| Category | Detail |
|---|
| Condition | Artificial Intelligence in Healthcare |
| Key Mechanisms | Integration of equity, diversity, and inclusion throughout the AI lifecycle. |
| Target Population | Populations facing barriers to accessing care, including unstable housing, migrants, individuals with disabilities, and those with low digital access. |
| Care Setting | Health and oral health care systems. |
Key Highlights
- AI risks reproducing existing disparities unless equity is integrated throughout its lifecycle.
- The EDAI framework provides guidance for integrating equity, diversity, and inclusion in healthcare.
- Invisible populations are often underrepresented in AI training datasets.
- Equity considerations are rarely prioritized in AI model development.
- Oral health is frequently excluded from digital health innovations.
Guideline-Based Recommendations
Diagnosis
- Incorporate social determinants of health in AI model development.
Management
- Utilize the EDAI framework to address barriers at micro, meso, and macro levels.
Monitoring & Follow-up
- Ensure ongoing evaluation of AI systems for equitable outcomes.
Risks
- AI systems may exacerbate health disparities if not designed with inclusivity in mind.
Patient & Prescribing Data
Vulnerable populations with complex health and social needs.
AI-driven care coordination systems can identify hidden risk factors.
Clinical Best Practices
- Engage stakeholders from diverse backgrounds in AI development.
- Prioritize equity as a core component of AI infrastructure.
- Provide adequate training and support for frontline healthcare workers using AI tools.
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